Introduction
In 1998, Barret et al. proffered that we are in a new age of ‘accelerated globalization of human disease ecologies’ or the ‘third epidemiological transition’. While the first ‘transition’ related to the increase in infectious disease in the Neolithic period and the second occurred with the ‘shift from infectious to chronic disease mortality’ in the Industrial Age (ibid). Taking this purview further, it may be argued that we are at the beginning of the ‘fourth epidemiological transition’ or climate change. However, as McMichael (Reference McMichael2009) offered: ‘paradoxically, modern epidemiological research has paid little attention to studying the health risks due to variations in climatic conditions.’
Historically, animal pathogens have received even less attention from the global research, health, and policy communities (La Rocque, Reference La Rocque2008; Reiter, Reference Reiter2008). The emergence (and recent reemergence) of zoonoses such as Sudden Acute Respiratory Syndrome (SARS), Highly Pathogenic Avian Influenza (HPAI), and Swine Flu has begun to change this state of affairs (Weiss and McMichael, Reference Weiss and McMichael2004). The role and impact of animal pathogens as global health and economic threats have been crucially recognized (Perry and Sones, Reference Perry and Sones2007; Slenning, Reference Slenning2008; Vallet, Reference Vallat2008; Gummow, Reference Gummow2010; Endy et al., Reference Endy, Rochford, Yuen and Lie2011; Lin and Heffernan, Reference Lin and Heffernan2011). For the first time in recent history, zoonotic diseases have raced up the global policy agenda. Nevertheless, the animal health community often appears to be ‘on the back foot’ with regard to meeting the challenges posed by climate change.
Our approach to exploring animal health and climate change tends to operate on the individual disease level, i.e. the role and impact of climate change on a specific disease; and as a body of work, the focus has been toward those diseases with well-known climate affects: arboviral, tick-borne, and parasitic diseases (Heffernan et al., Reference Heffernan, Salman and York2012). Further, across the literature, the many abiotic and biotic drivers to individual disease states have occupied much space. For example, in relation to arboviruses, Gould and Higgs (Reference Gould and Higgs2009) listed the drivers as follows: ‘levels of socioeconomic development; increasing human travel; commercial transportation; urbanization; deforestation; land reclamation; irrigation projects; human, animal, and arthropod population density increase; and political and military activities that lead to mass human evacuation.’ A similar set of macro-level changes have been described as part of the range of causal forces for parasitic disease (Patz et al., Reference Patz, Graczykb, Gellera and Vittor2000): ‘deforestation and ensuing changes in land use, human settlement, commercial development, construction of roads, water control systems (dams, canals, irrigation systems, reservoirs), and climate singly and in combination …’
When such forces are explored on an individual level, it is clear that particular drivers will have a greater or lesser impact on specific diseases and climate change is no exception. When these individual studies are collated, not surprisingly, little agreement is found on the role of climate change on animal health. Clearly the same drivers, in the same geographic area, are likely to influence a range of diseases across a variety of species. Undeniably such forces act on the collective rather than singular disease level, yet rarely do we consider disease as a collective.
Part of the problem is that disease itself is often viewed as an outcome of a linear sequence of drivers and pathways (Fig. 1).
Disease schemata.
However, it is increasingly recognized that disease processes are not linear. Equally problematic, viewing each driver as a discreet and unique factor ignores the inter-relationships and synergies between such factors important to disease emergence, transmission, and spread. Consequently, prioritizing the importance of different factors rarely takes place on the individual or collective level. Therefore, our historical approach to understanding disease, while singular and explicit, has produced a highly polarized knowledge base that offers little regarding explicating the overall role of climate change on animal health. Clearly, climate, and therefore climate change, is a context rather than a driver. As such, climate change provides both a background to, and a range of, direct and indirect factors influencing both human and animal disease states. In this manner, explorations that focus exclusively on climate are equally problematic and likely to miss the complex interplay of both initiating and impacting forces on disease processes.
This is not to say, however, that unpacking the relationship between the environment and disease has been of little interest to researchers. In recent years, a number of frameworks have been offered.
From eco-epidemiology to epi-systems
Within the field of human health, the concept of ‘eco-epidemiology’ has been described (Susser and Susser, Reference Susser and Susser1996; Susser, Reference Susser2004; March and Susser, Reference March and Susser2006; Kirkbride and Jones, Reference Kirkbride and Jones2011). The approach arose from the desire to broaden the focus of epidemiological studies from the individual level to the societal level and thereby, enabling a greater contribution to the field of public health (Mackenbach, Reference Mackenbach1998). Early work conceptualized eco-epidemiology as combining the results of individual studies to respond to a single issue (Susser, Reference Susser2004). Over the years, a wider more inclusive concept has developed, as Kirkbride and Jones (Reference Kirkbride and Jones2011)noted:
…the concept of “eco-epidemiology” offers a theoretical framework within which we might integrate factors between several layers of causation, including genetic, epigenetic, individual, familial, community, and societal influences. The approach emphasizes understanding the interconnectedness and interdependence of these levels, their developmentally sensitive nature over the life course, and the wider socio-temporal contexts that shape disease risk.
In practice, however, most articles adopting the approach focus on the interaction of the disease in question with a range of environmental factors as opposed to better understanding the ‘interconnectedness or interdependence’ of the variety of factors contributing to disease states (Eisen et al., Reference Eisen, Mead, Meyer, Pfaff, Bradley and Eisen2008; Singer et al., Reference Singer, Abramson, Shoob, Zaken, Zentner and Stein-Zamir2008; Kirkbride and Jones, Reference Kirkbride and Jones2011). In short, many eco-epidemiological studies appear to simply adopt more ‘spatially explicit approaches’ (Ostfeld et al., Reference Ostfeld, Glass and Keesing2005).
Similarly, in the field of animal health, Tabachnick (Reference Tabachnick2010) described the ‘epi-systems’ approach. Where an epi-system is the ‘vectors, the hosts, the pathogens, the biological controlling mechanisms and all of the environmental factors that have an effect on disease epidemiology within a defined spatiotemporal region’. However, while the approach lists the many and disparate factors important to disease spread, no attempt is made to prioritize or assess the likely differing impact and importance of these factors over time. At present, the adoption of the framework within the animal health literature appears limited.
Therefore, while more recent approaches attempt to integrate the range of biotic and abiotic factors important to animal disease states, it is clear that climate remains a separate and distinct driver to these processes. In order to advance the field such holistic approaches, in and of themselves, while necessary are insufficient.
Climate-change syndemics
Explicating the role and impact of climate change on human and animal health requires a framework that explores the interactions between causal factors and climate change and the inter-relationships between infectious diseases themselves within a context of climate change; and in this manner, the focus of analysis logically changes from explorations of the individual disease to collective human and animal health infectious diseases across populations/geographies and their inter-relationships.
Within the field of global health, a syndemic is widely defined as the occurrence of two or more diseases or syndromes, which by interacting synergistically increase the burden of disease in a population (Singer et al., Reference Singer, Abramson, Shoob, Zaken, Zentner and Stein-Zamir2008; Rock et al., Reference Rock, Buntain, Hatfield and Hallgrímsson2009). Critical to the concept is the notion of synergy and interaction (Singer et al., Reference Singer, Abramson, Shoob, Zaken, Zentner and Stein-Zamir2008; Rock et al., Reference Rock, Buntain, Hatfield and Hallgrímsson2009). Within the context of this article, I am extending the conceptual notion away from the identification of a particular disease to include all of the abiotic and biotic factors that ‘interact synergistically’ to support the emergence or reemergence of infectious disease (both human and animal) under conditions of climate change.
Therefore, climate-change syndemics operates on two levels: first at the ‘collective’ or total human and animal infectious disease burden in any given geography, and second at the interface in which inter-relationships, synergies between these collective infectious disease states and abiotic and biotic factors operate. In this manner, the point of observation is climate change as an embedded risk to a number of concurrent infectious diseases across species rather than as an external driver to a particular event or singular disease.
On another level, this moves the frame of analysis from the traditional and largely linear ‘cause and effect’ to what is likely to be perceived as the less satisfactory ‘cause and interaction’ or even ‘interaction and synergy’. But in moving away from the singular and particular, the outcome of these explorations will ultimately yield a richer and more nuanced understanding of infectious disease and climate change. Such a move clearly demands new methodological approaches, and therein lies the rub.
At present our approach to disease modeling largely relies on ‘outcome measures’ such as R 0 or in the field of human health deterministic constructs such as the SIRs (susceptible, infected, and recovered) model and its many variations. Such approaches largely start from the disease and work backwards to unpack the causal factors of an epidemic. Nevertheless, there are large differences between human versus animal infectious disease modeling and priority setting (Heffernan, Reference Heffernan2009). Therefore, exploring infectious disease on a collective level brings forth a number of methodological challenges.
First, there is no equivalent DALY or QALY parameter in animal health. The DALY is a human health metric that measures the ‘Disability Adjusted Life Years’ or the overall disease burden expressed as the number of years lost to ill health (Heffernan, Reference Heffernan2009). While the QALY is a measure of the disease burden, which reflects both the ‘Quantity’ and ‘Quality’ of life lived. In creating a combined impact metric for human and animal health, the main issue is as follows: the longevity of a livestock life is rather not important. However, the impacts of livestock disease on the lives and livelihoods of the over 1.1 billion poor people who keep them, are. Therefore, any combined metric must account for both human well-being and the poverty impacts of livestock disease.
Second, at its most fundamental level, such a collective approach requires a shift from outcome to ‘cascade measures’. Where cascade measures are defined as incremental impacts and influences of change across a range of social, environmental, climate, economic, and epidemiological factors important to identifying the interplay between climate change and disease. In determining such measures, it is clear that there are different layers of impacts on different scales that must be disaggregated. Climate change will affect livestock production at the local, national, and global scales by altering the landscapes of production. On the broadest level, such shifts will change the geographic location of hosts and therefore, the subsequent patterns of disease. At the micro-level, host susceptibility and infectiousness will also be affected.
Conclusions
Clearly the literature on animal health and climate change is still in its infancy, with large gaps particularly regarding causal relationships and, of equal importance, the methods or approaches required to determine such relationships. But more critically, the gaps in knowledge regarding animal health and climate change are not likely to be sufficiently filled via the present reliance on the outcome-based methods. Such approaches lack the means to unpack the constellation and cascade effects of climate change on the environmental, demographic, and socio-economic forces implicit to the host–pathogen–disease nexus. Thus, I argue for a move to ‘climate-change syndemics’ with a focus on the development of tools and approaches to better delineate these forces on animal infections both present and future.
In this manner, climate-change syndemics can no longer be constrained by national boundaries, economies, and social systems. Rather, in meeting the needs of the ‘fourth epidemiological transition’, we need a global epidemiology, which accounts for, but crucially transcends these issues based on explorations of inter-relationships rather than simply outcomes.
